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The advent of high-throughput sequencing technologies has lead to vastcomparative genome sequences. The construction of gene-gene interactionnetworks or dependence graphs on the genome scale is vital for understandingthe regulation of biological processes. Different dependence graphs can providedifferent information.  Some existing methods for dependence graphs based on high-order partialcorrelations are sparse and not informative when there are latent variablesthat can explain much of the dependence in groups of genes. Other methods ofdependence graphs based on correlations and first-order partial correlationsmight have dense graphs. When genes can be divided into groups with strongerwithin group dependence in gene expression than between group dependence, wepresent a dependence graph based on truncated vines with latent variables thatmakes use of group information and low-order partial correlations. The graphsare not dense, and the genes that might be more central have more neighbors inthe vine dependency graph. We demonstrate the use of our dependence graphconstruction on two RNA-seq data sets -- yeast and prostate cancer. There issome biological evidence to support the relationship between genes in theresulting dependence graphs.  A flexible framework is provided for building dependence graphs via low-orderpartial correlations and formation of groups, leading to graphs that are nottoo sparse or dense. We anticipate that this approach will help to identifygroups that might be central to different biological functions.

Vine dependence graphs with latent variables as summaries for gene expression data